Using Magnetic Resonance Perfusion to Stratify Overall Survival in Treated High-Grade Gliomas
Bibliographic record
Abstract
BACKGROUND: MR perfusion imaging is a relatively new technique that may aid in identifying recurrent tumor (RT) in those with radically treated high-grade gliomas (HGG). We aim to assess the relationship between dynamic susceptibility contrast-enhanced MR perfusion (DSC-MRP) and overall survival to establish a baseline for future research and to determine the utility of DSC-MRP as a clinical decision-making and prognostic tool. METHODS: We conducted a retrospective cohort study. Adults with pathologically confirmed HGG at the Juravinski Cancer Centre, Ontario between January 2011 and April 2014 with at least one post-treatment DSC-MRP were included. DSC-MRP was interpreted as positive or negative for tumor recurrence by experienced radiologists. The primary outcome was overall survival. RESULTS: Sixty-one patients were enrolled. Median survival for patients with a positive DSC-MRP scan was 4.5 months compared with 10.2 months for those with a negative DSC-MRP scan (hazard ratio [unadjusted] = 2.51; 95% confidence interval = 1.10-5.67; p-value = 0.03). Multivariable modeling (adjusted) that included all pre-selected variables showed similar results. CONCLUSION: Survival time in patients with HGG is generally low, and almost all patients will demonstrate RT. Our data suggest a positive DSC-MRP correlates with lower overall survival and may signify the presence of highly active RT. These results generate a hypothesis that there may be a prognostic role for the use of serial DSC-MRP for tumor surveillance. More importantly, this biomarker may aid in decision making for treatment plans and palliation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".